Dataset Information

The somatic genomic landscape of glioblastoma.

ABSTRACT: We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.

SUBMITTER: Brennan CW

PROVIDER: S-EPMC3910500 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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The somatic genomic landscape of glioblastoma.

Brennan Cameron W CW Verhaak Roel G W RG McKenna Aaron A Campos Benito B Noushmehr Houtan H Salama Sofie R SR Zheng Siyuan S Chakravarty Debyani D Sanborn J Zachary JZ Berman Samuel H SH Beroukhim Rameen R Bernard Brady B Wu Chang-Jiun CJ Genovese Giannicola G Shmulevich Ilya I Barnholtz-Sloan Jill J Zou Lihua L Vegesna Rahulsimham R Shukla Sachet A SA Ciriello Giovanni G Yung W K WK Zhang Wei W Sougnez Carrie C Mikkelsen Tom T Aldape Kenneth K Bigner Darell D DD Van Meir Erwin G EG Prados Michael M Sloan Andrew A Black Keith L KL Eschbacher Jennifer J Finocchiaro Gaetano G Friedman William W Andrews David W DW Guha Abhijit A Iacocca Mary M O'Neill Brian P BP Foltz Greg G Myers Jerome J Weisenberger Daniel J DJ Penny Robert R Kucherlapati Raju R Perou Charles M CM Hayes D Neil DN Gibbs Richard R Marra Marco M Mills Gordon B GB Lander Eric E Spellman Paul P Wilson Richard R Sander Chris C Weinstein John J Meyerson Matthew M Gabriel Stacey S Laird Peter W PW Haussler David D Getz Gad G Chin Lynda L

Cell 20131001 2

We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is con ...[more]

PMID: 24120142

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Project description:Introduction: Glioblastoma multiforme (GBM) is the most common deadly brain malignancy and lacks effective therapies. Immunotherapy acts as a promising novel strategy, but not for all GBM patients. Therefore, classifying these patients into different prognostic groups is urgent for better personalized management. Materials and Methods: The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to estimate the fraction of 22 types of immune-infiltrating cells, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune infiltration-related prognostic scoring system (IIRPSS). Additionally, a quantitative predicting survival nomogram was also established based on the immune risk score (IRS) derived from the IIRPSS. Moreover, we also preliminarily explored the differences in the immune microenvironment between different prognostic groups. Results: There was a total of 310 appropriate GBM samples (239 from TCGA and 71 from CGGA) included in further analyses after CIBERSORT filtering and data processing. The IIRPSS consisting of 17 types of immune cell fractions was constructed in TCGA cohort, the patients were successfully classified into different prognostic groups based on their immune risk score (p = 1e-10). What's more, the prognostic performance of the IIRPSS was validated in CGGA cohort (p = 0.005). The nomogram also showed a superior predicting value. (The predicting AUC for 1-, 2-, and 3-year were 0.754, 0.813, and 0.871, respectively). The immune microenvironment analyses reflected a significant immune response and a higher immune checkpoint expression in high-risk immune group. Conclusion: Our study constructed an IIRPSS, which maybe valuable to help clinicians select candidates most likely to benefit from immunological checkpoint inhibitors (ICIs) and laid the foundation for further improving personalized immunotherapy in patients with GBM.

Dataset Information

The somatic genomic landscape of glioblastoma.

Publications

The somatic genomic landscape of glioblastoma.

Similar Datasets

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